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A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis

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  • ANGHEL, Dan-Gabriel

Abstract

This paper performs a reality check for the superior predictive ability of Machine Learning and Technical Analysis trading rules in the cryptocurrency market. After controlling for data snooping and various market frictions, we find that statistically significant positive excess returns are rarely achieved, independent of the data sampling frequency, type of trading position, or test significance level. Also, cross-sectional performance is correlated with risk factors such as beta and idiosyncratic volatility, implying that trading rules mostly capture market risk premiums. Overall, trading rules do not seem to provide additional benefits in cryptocurrency markets compared to traditional financial markets.

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  • ANGHEL, Dan-Gabriel, 2021. "A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis," Finance Research Letters, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:finlet:v:39:y:2021:i:c:s1544612320304414
    DOI: 10.1016/j.frl.2020.101655
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    Cited by:

    1. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    2. Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
    3. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.
    4. Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).

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    More about this item

    Keywords

    Reality check; Data snooping; Cryptocurrency market; Trading rules; Machine learning; Technical analysis;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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